The present invention relates generally to machine diagnostics, and more specifically, to a system and method for hybrid processing of snapshot operational parameter data and fault log data to facilitate analysis of a machine undergoing diagnostics.
A machine, such as a locomotive or other complex systems used in industrial processes, medical imaging, telecommunications, aerospace applications, power generation, etc., includes elaborate controls and sensors that generate faults when anomalous operating conditions of the machine are encountered. Typically, a field engineer will look at a fault log and determine whether a repair is necessary.
Approaches like neural networks, decision trees, etc., have been employed to learn over input data to provide prediction, classification, and function approximation capabilities in the context of diagnostics. Often, such approaches have required structured and relatively static and complete input data sets for learning, and have produced models that resist real-world interpretation.
Another approach, Case Based Reasoning (CBR), is based on the observation that experiential knowledge (memory of past experiencesxe2x80x94or cases) is applicable to problem solving as learning rules or behaviors. CBR relies on relatively little pre-processing of raw knowledge, focusing instead on indexing, retrieval, reuse, and archival of cases. In the diagnostic context, a case refers to a problem/solution description pair that represents a diagnosis of a problem and an appropriate repair.
CBR assumes cases described by a fixed, known number of descriptive attributes. Conventional CBR systems assume a corpus of fully valid or xe2x80x9cgold standardxe2x80x9d cases that new incoming cases can be matched against.
U.S. Pat. No. 5,463,768 discloses an approach which uses error log data and assumes predefined cases with each case associating an input error log to a verified, unique diagnosis of a problem. In particular, a plurality of historical error logs are grouped into case sets of common malfunctions. From the group of case sets, common patterns, i.e., consecutive rows or strings of data, are labeled as a block. Blocks are used to characterize fault contribution for new error logs that are received in a diagnostic unit. Unfortunately, for a continuous fault code stream where any or all possible fault codes may occur from zero to any finite number of times and where the fault codes may occur in any order, predefining the structure of a case is nearly impossible.
U.S. patent application Ser. No. 09/285,611, assigned to the same assignee of the present invention and herein incorporated by reference, discloses a system and method for processing historical repair data and fault log data, which is not restricted to sequential occurrences of fault log entries and which provides weighted repair and distinct fault cluster combinations, to facilitate analysis of new fault log data from a malfunctioning machine. Further, U.S. patent application Ser. No. 09/285,612, assigned to the same assignee of the present invention and herein incorporated by reference, discloses a system and method for analyzing new fault log data from a malfunctioning machine in which the system and method are not restricted to sequential occurrences of fault log entries, and wherein the system and method predict one or more repair actions using predetermined weighted repair and distinct fault cluster combinations.
It is believed that the inventions disclosed in the foregoing patent applications provide substantial advantages and advancements in the art of diagnostics. It would be desirable, however, to provide a system and method that uses snapshot observations of operational parameters from the machine in combination with the fault log data in order to further enhance the predictive accuracy of the diagnostic algorithms used therein. It would be further desirable, through the use of noise reduction filters, to substantially eliminate undesirable noise, e.g., unreliable or useless information that may be present in the fault log data and/or the operational parameter data. This noise reduction would advantageously allow for increasing the probability of early detection of actual incipient failures in the machine, as well as decreasing the probability of falsely declaring non-existent failures.
Generally speaking, the present invention fulfills the foregoing needs by providing a method for analyzing fault log data from a machine undergoing diagnostics. A receiving step allows for receiving fault log data comprising a plurality of faults from the machine. Respective executing steps allow for executing a set of noise-reduction filters upon the received fault log data to generate noise-reduced fault log data, and for executing a set of candidate snapshot anomalies upon the noise-reduced data to generate data predictive of malfunctions of the machine.
In another embodiment, the methods allows for receiving fault log data including a plurality of faults from the machine and for receiving operational parameter data including a plurality of operational parameters from the machine. A executing step allows for executing a set of candidate snapshot anomalies upon the fault log data and upon the operational parameter data. A combining step allows for combining any candidate snapshot anomalies respectively triggered by the fault log data with any candidate snapshot anomalies respectively triggered by the parameter data to generate data predictive of malfunctions of the machine.
The present invention further fulfills the foregoing needs by providing a system for analyzing fault log data and operational parameter data from a machine undergoing diagnostics. The system includes means for receiving fault log data including a plurality of faults from the machine. A memory unit is configured to store a set of noise-reduction filters and a set of candidate snapshot anomalies. A processor is respectively coupled to the means for receiving fault log data and to the memory unit. The processor includes means for executing the set of noise-reduction filters upon the received fault log data to generate noise-reduced fault log data, and further includes means for executing the set of candidate snapshot anomalies upon the noise-reduced data to generate data predictive of malfunctions of the machine.